OCHI CfP Data/Algorithms/Models: Prototyping with Uncertainty in Research through Design

[Baptiste Caramiaux]

WE HAVE EXTENDED THE DEADLINE TO JUNE 30!

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Special Issue of ACM Transactions on Computer-Human Interaction (TOCHI) - Call for Papers (CFP)

Data/Algorithms/Models: Prototyping with Uncertainty in Research through Design

Special issue editors: Elisa Giaccardi (Delft University of Technology, The Netherlands), Dave-Murray Rust (Delft University of Technology, The Netherlands), Johan Redström (Umeå Institute of Design, Sweden), Baptiste Caramiaux (Sorbonne Université, CNRS, France)

Contact: Elisa Giaccardi, e.gia...@tudelft.nl 

NEW Deadline for Submissions: June 30th, 2023

The community of Research through Design (RtD) practitioners that make things that sense, log and react to data streams is growing—as grows the struggle of understanding how forms of RtD that experiment with data and algorithms are different from RtD traditions of making and prototyping informed by practices of skillful crafting and industrial design manufacturing (Giaccardi 2019). The labor is not just technical (Dove, Halskov, Forlizzi, & Zimmerman, 2017; Yang, Scuito, Zimmerman, Forlizzi, & Steinfeld, 2018), nor is aligning design and research intentions just a matter of interdisciplinary collaboration (Basballe & Halskov, 2012). In addition, working with diverse streams of data brings new aspects of connectivity and context to design, driving a shift from the typical stand-alone objects to networked systems and otherwise entangled assemblages (Höök & Löwgren 2021, Redström & Wiltse 2018).

Data brings changes to RtD practice that extend far beyond a shift in materiality, from primarily physical to also computational. It implies working with unstable objects that are defined not just by their initial form and intended use, but by the sustained feedback loops between design and use that data technologies make possible.

As we leave behind industrial conceptions of the prototype to consider decentralized, connected things, and we begin considering digital things as capable of making things too (Giaccardi & Redström 2020, Wiltse 2020, Redstrom & Wiltse 2018), algorithmic practices of RtD must engage shifts in design practice that concern the agentive and infrastructural roles that non-human makers such as algorithms may take in decentralized making processes.

Present RtD practices often emphasize the importance of using data as a design material for the purpose of exploring and co-creating with users new design directions (Bogers, Frens, van Kollenburg, Deckers, & Hummels, 2016; Zimmerman et al., 2011). Emphasis is often also placed on the need to gain deeper insights into user experience by integrating quantitative and qualitative methods for long periods of time and engaging with users continuously and remotely (van Kollenburg et al., 2018).

However, because algorithms also make judgments and perform actions that create new connections and shape new relations with people and machines, the design that they enable and promote is no longer a “stabilizing process” (Gunn & Donovan, 2012). Unstable, probabilistic, and agentive, the prototypes created by algorithmic practices of RtD perform and actively take part in a decentralized making process through which future practices are virtually experimented with and endlessly reconfigured (Giaccardi 2019). This interplay of human and nonhuman resources profoundly challenges the stabilizing character of the artifact. Reconceptualizing the things we make as capable of making things too shifts the locus of doing design towards a fundamentally recursive and probabilistic relation between design and use, and between producer and produced—a new relationship that needs to aesthetically, ethically, and politically balance and integrate capabilities and doings uniquely human and uniquely artificial (Kuijer & Giaccardi, 2018; Giaccardi 2019). 

This fundamental shift opens new roles for designerly processes of knowledge production as well as new perspectives on data in design. For example, while A/B testing and continuous monitoring of user behavior have become essential aspects of design practice in the data-driven domain, it has also become increasingly clear that automating design decisions risks reducing design to construction and optimization of already defined trajectories (Gorkovenko et al. 2020). Given what is at stake as data-enabled artifacts and AI systems permeate and govern our everyday lives, algorithmic practices of RtD offer an important experimentation space for imaginative and critical perspectives on the relations between design and data, and how these can be put into practice.

In this special issue, we aim to articulate and explore what working with data-enabled, connected things, machine learning algorithms, and computational models means in fields that position iterative processes of design experimentation as central to the production of knowledge. Examples of these fields are Research through Design (RtD), constructive design research, and similar practice-based design research approaches. We seek to reach out to a broad canvas of contributors and perspectives, including work from southern and non-western areas of the world that may resonate with this call, and to illuminate the types of questions design researchers/practitioners should be asking as they move into algorithmic practices. These questions include but are not limited to:

• How do we understand changes in agency as artifacts flow from collection through data into algorithmic models and systems? How do we understand the changes in responsibility and accountability that this brings?
• How do we understand, and creatively work with, the fundamental difference between the design decisions algorithms are able to make, and the (design) judgments of designers/users?
• How can  practitioners (and/with users) engage with prototyping in these complex, technological situations, both with and without substantial computational resources?
• What design methodologies do practitioners need to develop for working with prototypes characterized by their temporalities rather than the spatial presence that traditional design methods focus on?
• How can data direct designers’ and users’ attention from individual and social practices to larger-scale organizational and political perspectives?
• What are the affordances, metaphors/figurations, and data practices around data-driven infrastructures that can support co-operation over co-option?
• How do we do meaningful knowledge generation and sharing around data when knowledge is in flux, and challenging to interpret? When designers are not trained in data science, let alone when interpretations are reflexively fed back into the situation as the data-driven artifacts and algorithms adapt and change? And when it is not transparent who/what is sharing what with whom/what?

Topics may include, but are not limited to:

• Unstable/probabilistic artifacts
• Prototyping with uncertainty
• Decentralized making processes
• Planetary scales and temporalities
• More-than-human agency and frames 
• Privacy and accountability issues
• Transparency of human labor and data work

All contributions will be rigorously peer reviewed to the usual exacting standards of TOCHI. Further information, including TOCHI submission procedures and advice on formatting and preparing your manuscript, can be found at: http://www.acm.org/tochi/

Manuscripts are submitted via the ACM online manuscript system at:
http://acm.manuscriptcentral.com/tochi/

To discuss a possible contribution, please contact the special issue editors at e.gia...@tudelft.nl.

Updated Schedule 

Submissions due June 30th, 2023
Reviews due September 30th, 2023
Notification to authors October 15th, 2022
Revised version due December 1st, 2023
Special issue published TBD


REFERENCES

• Basballe, D. A., & Halskov, K. (2012). Dynamics of research through design. In Proceedings of the 2012 DIS Conference on Designing Interactive Systems (pp. 58-67). New York, NY: ACM.
• Bogers, S., Frens, J., van Kollenburg, J., Deckers, E., & Hummels, C. (2016). Connected baby bottle: A design case study towards a framework for data-enabled design. In Proceedings of the 2016 DIS Conference on Designing Interactive Systems (pp. 301-311). New York, NY: ACM. doi:10.1145/2901790.2901855 
• Dove, G., Halskov, K., Forlizzi, J., & Zimmerman, J. (2017). UX design innovation: Challenges for working with machine learning as a design material. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 278-288). New York, NY: ACM. 
• Gorkovenko, K., Burnett, D.J., Thorp, J.K., Richards, D., Murray-Rust, D. (2020). Exploring the future of data-driven product design. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (1-14). New York, NY: ACM.
• Giaccardi, E. (2019). Histories and futures of research through design: From prototypes to connected things. International Journal of Design, 13(3), 139-155. 
• Giaccardi, E. & Redström, J. (2020).  Technology and more-than-human design. Design Issues, (36)4, 33-44.
• Gunn, W., & Donovan, J. (2012). Design anthropology: An introduction. In W. Gunn & J. Donovan (Eds.), Design and anthropology. London, UK: Routledge. 
• Höök, K. & Löwgren, J. (2021). Characterizing Interaction Design by Its Ideals: A Discipline in Transition. She Ji: The Journal of Design, Economics, and Innovation, 7(1), 24-40.
• Kuijer, L. & Giaccardi, E. (2018). Co-performance: Conceptualizing the role of artificial agency in the design of everyday life. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (1-14). New York, NY: ACM.
• Redström, J., & Wiltse, H. (2018). Changing things: The future of objects in a digital world. London, UK: Bloombsury Academic.
• Van Kollenburg, J., Bogers, S., Rutjes, H., Deckers, E., Frens, J., & Hummels, C. (2018). Exploring the value of parent tracked baby data in interactions with healthcare professionals: A data-enabled design exploration. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (No. 297). New York, NY: ACM. 
• Wiltse, H. (2020) (Ed.) Relating to things: Design, technology and the artificial. London, UK: Bloombsury Academic.
• Yang, Q., Scuito, A., Zimmerman, J., Forlizzi, J., & Steinfeld, (2018). Investigating how experienced UX designers effectively work with machine learning. In Proceedings of the 2018 DIS Conference on Designing Interactive Systems (pp. 585-596). New York, NY: ACM.
• Zimmerman, J., Tomasic, A., Garrod, C., Yoo, D., Hiruncharoenvate, C., Aziz, R., … Steinfeld, A. (2011). Field trial of Tiramisu: Crowd-sourcing bus arrival times to spur co-design. In Proceedings of the 2011 CHI Conference on Human Factors in Computing Systems (pp. 1677-1686). New York, NY: ACM.